Retail AI Solutions
AI and Analytics for Retail Solutions
Retail Reinvented: Analytics & AI for Smarter Decisions
Customers who have negative shopping experiences like payment failures, slow checkout, out-of-stock items, or confusing navigation will switch to another retailer without hesitation. AI when used intentionally and meaningfully, can help reduce churn.
The global retail analytics market is projected to reach USD 47.38 Billion by 2030, it’s clear that data-driven decision-making is the backbone of modern retail.
We use Python-based predictive models to forecast demand to prevent overstock and stockouts. Tableau and Power BI create real-time dashboards for instant insights into sales trends, stock levels, and customer behavior. Natural language processing (NLP) powers sentiment analysis to find what your customers say online, while machine learning algorithms personalize shopping journeys based on past behaviors.
Imagine being able to predict which products will be your top sellers in the next holiday season. AI retail solutions ensure that your shelves are stocked with what customers want, helping you save on inventory costs while maximizing revenue.
50%
Increase in Customer Retention
40%
Lower
Inventory Costs
2x
Faster Decision Making
Let’s make retail decisions twice as fast with AI-driven insights. Start today
AI & Analytics for Retail Success
With a decade of experience, we’ve mastered using AI to transform retail operations. With data, we can tell what your customers want – be it product choices or flexible delivery and pickup options, loyalty rewards, easy checkout processes, and much more.
Stocking the right products at the right time ensures shelves are always filled with what customers want. With AI-powered demand forecasting and inventory optimization you optimize for max sales & avoid wasting resources.
We use Python-based predictive models to analyze historical data, seasonal trends, and market dynamics. This allows you to predict demand and avoid overstocking or stockouts accurately. Power BI provides real-time insights into inventory, demand changes, and stock levels for quick decision-making. Machine learning algorithms identify slow-moving products, prioritize restocking high-demand items, and reduce holding costs.
For example, a retailer preparing for the holiday season predicted a surge in demand for winter jackets in colder regions. They adjusted inventory placement to match demand, avoided overstock, cut costs, and ensured customers got what they needed.
Imagine you deliver what your customers want before they even ask. With customer behavior analytics, you know their preferences, buying patterns, and habits.
Heap tracks user interactions across your website and app to reveal how customers engage with products. Mixpanel segments your audience based on behavior, helps you focus on the right customers with the right offers.
Meanwhile, Amplitude maps out the customer journey to show what works and where customers drop off. These insights let you create targeted promotional campaigns, optimize product recommendations, and improve the shopping experience.
For example, If a retailer finds users abandoning carts due to high shipping costs, introduce free delivery thresholds and reduce cart abandonment– increased completed purchases.
Real-time pricing allows you to adjust prices based on live demand, customer behavior, and market trends. We use dynamic pricing algorithms to analyze real-time data for product demand and market fluctuations. Google BigQuery processes vast amounts of data quickly to provide actionable insights to refine pricing strategies.
ElasticSearch tracks competitor prices and updates your pricing instantly to attract more customers. Kibana dashboards provide real-time visibility into stock levels. This helps you to discount slow-moving products for quicker turnover and price high-demand items to maximize revenue.
For example, a retailer launching a summer collection increases the price of high-demand swimwear and applies discounts to slower-moving products– hats and sandals.
Targeted marketing starts with understanding your customers. Basic segmentation mistakes – grouping all millennials as digital-first shoppers, targeting parents with teen products, or sending discount offers to luxury buyers – waste your marketing dollars on mismatched audiences.
To address this, we use Microsoft Azure Synapse to process large datasets and group customers based on demographics, purchase history, and browsing habits. Looker provides in-depth visualization of these segments, offering clear insights for dynamic campaign adjustments. Machine learning algorithms in SciKit-Learn predict future behavior to target the right audience and offer personalized offers.
For example, a retailer identifies customers who buy running shoes but not accessories like socks or water bottles. By offering discounts on these items at checkout, the retailer increases accessory sales and avoids spending on uninterested customers.
Understand the long-term value of your customers to make smarter, more profitable decisions. Predict which customers will generate the most revenue over time– tailor your marketing efforts, retention strategies, and product offerings to maximize profitability according to the predicted insights.
We use machine learning algorithms to analyze customer purchase history, behavior, and engagement patterns. This helps you segment customers based on their predicted lifetime value (CLV) and identify high-potential customers. We combine historical data with predictive models to forecast the future value of each customer segment– focus your resources on those most likely to drive growth.
For example, a retailer discovers that 20% of customers drive 80% of revenue. By targeting these high-value customers with personalized offers and loyalty programs, they can increase repeat purchases and boost retention rates.
Understand customer foot traffic and how they move through your store to improve conversion rates. Without this data, you miss opportunities to improve in-store experiences and maximize sales.
We use sensor-based analytics and video tracking technologies to monitor store foot traffic and understand customer navigation across different sections of the store. This data reveals high-traffic areas, bottlenecks, and performance of displayed products. Heatmaps and conversion modeling help us optimize store layouts and promotional displays so that the right products are in front of customers at the right time.
For example, If you use store traffic analysis to find that shoppers spend less time in the electronics section. You could relocate high-demand items to this area and improve the displays– sales increased without changing the layout.
A consistent shopping experience across all channels is non-negotiable. Without integrated data, you risk fragmented customer journeys and missed opportunities for cross-channel engagement.
We use Microsoft Azure Synapse to aggregate and analyze data from online platforms, physical stores, mobile apps, and customer service interactions. This unified view lets us track customer behavior and create a seamless experience across touchpoints. AI-powered tools deliver real-time insights that enable personalized offers, trend predictions, and operational improvements. Our system adapts quickly and adjusts strategies based on customer engagement with your brand at every stage.
For example, imagine you track a customer who browses online and later visits the store. Send personalized discounts for items they viewed online and increase both in-store and online conversions – delighting the customer, not letting them know how you did it.
The Codewave Way: Strategic, Data-Driven, Measurable
We keep it simple: data drives everything, but we ensure it works for you. Our approach is to solve your real problems, with the tools, insights, and strategies to make it happen.
The Retail AI Toolkit That Moves the Needle
| Technology Category | Tools/Technologies |
| Data Processing | Google BigQuery, Microsoft Azure Synapse |
| Customer Behavior Analytics | Heap, Mixpanel, Amplitude |
| AI/ML Modeling | SciKit-Learn, TensorFlow, PyTorch |
| Visualization & Reporting | Tableau, Looker, Power BI |
| Real-Time Insights | Kibana, Grafana |
| Integration | Zapier, RESTful APIs |
| Security | ISO-certified Cloud Storage, Encryption |
Real Feedback, Real Impact
Don’t just listen to us—hear it from the retailers making moves with AI. They’re not just talking the talk—they’re seeing real results
View our portfolio!
See Our Success Stories in Action
Ready to see AI in action? Our case studies show how we helped retailers crush it with smarter pricing, inventory, and customer insights. Don’t just take our word for it—check out the results and see how we can do the same for you.
We transform companies!
Codewave is an award-winning company that transforms businesses by generating ideas, building products, and accelerating growth.
Frequently asked questions
Retail AI refers to the application of artificial intelligence technologies in various aspects of retail operations. It can benefit your business by enhancing customer experiences, optimizing inventory management, personalizing marketing efforts, providing predictive analytics for better decision-making, and streamlining operations. These benefits lead to increased efficiency, improved customer satisfaction, and ultimately, higher revenue and growth.
Codewave’s approach is unique because we combine design thinking methodologies with cutting-edge AI technologies. This means we don’t just implement AI solutions, but we first deeply understand your business challenges and customer needs. Our design thinking-led approach ensures that the AI solutions we develop are not only technologically advanced but also user-centric and aligned with your business goals.
AI solutions can benefit a wide range of retail businesses, from small and medium-sized enterprises to large corporations. This includes brick-and-mortar stores, e-commerce platforms, omnichannel retailers, and specialized retail sectors such as fashion, electronics, groceries, and more. The key is to tailor the AI solutions to the specific needs and challenges of each business.
Accordion ContentThe implementation timeline can vary depending on the complexity of the solution and the size of your business. Generally, basic AI implementations can start showing results within 3-6 months, while more complex, enterprise-wide solutions may take 6-12 months to fully implement and demonstrate significant results. However, you’ll often see incremental improvements throughout the implementation process.
At Codewave, we prioritize data security and comply with international data protection regulations. We implement robust security measures to protect your data, including encryption, secure cloud storage, and strict access controls. We also ensure that all AI models are trained and operated in compliance with data privacy laws, giving you peace of mind about the safety of your valuable business and customer data.
Getting started is easy. You can book a free consultation with our team of experts who will assess your current operations, discuss your goals, and recommend tailored AI solutions for your business. We’ll guide you through the entire process, from initial assessment to implementation and ongoing support. Contact us today to take the first step towards transforming your retail business with AI.
AI can optimize pricing by analyzing customer behavior, competitor prices, and market trends in real-time. AI-driven dynamic pricing models adjust prices automatically based on demand fluctuations, inventory levels, and competitor pricing. This allows you to maximize revenue, reduce the risk of overpricing or underpricing, and stay competitive in the market.
AI improves inventory management by predicting demand more accurately and optimizing stock levels. By analyzing historical data and trends, AI helps avoid overstocking and stockouts, ensuring you always have the right amount of product at the right time. AI also aids in automating restocking and distribution, minimizing operational costs while maximizing product availability.
Yes, AI can significantly enhance customer service. AI-driven chatbots and virtual assistants provide instant, round-the-clock support, resolving customer queries and issues faster than traditional methods. AI can also help personalize the customer experience by analyzing past interactions, offering product recommendations, and even predicting future needs, ensuring higher customer satisfaction and loyalty.
The ROI from implementing AI in retail varies based on the solutions deployed, but businesses typically see improvements in sales, operational efficiency, and customer satisfaction. For example, AI can reduce inventory costs by predicting demand accurately, increase sales through personalized recommendations, and improve customer retention by enhancing the shopping experience. Many businesses experience measurable returns within the first year of implementing AI, with continued growth over time.
Latest thinking
Ride the waves of Change.
What excites us is ‘Change’. We love watching our customer’s business transform after coming in touch with us.